International Journal of Research and Scientific Innovation (IJRSI)
Mixture of Experts (MoE) Based Top Performer Segmentation with Multilingual Chatbot Integration
Published May 8, 2026 • Vol. 13, Issue 4, pp. 1713–1724Open Access
DOI: 10.51244/IJRSI.2026.1304000149
Abstract
This paper presents a Mixture of Experts (MoE) architecture for workforce segmentation. The proposed framework combines multiple machine learning models—Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Artificial Neural Network (ANN)—using a softmax-based gating network to dynamically assign weights to expert predictions. The system is evaluated on large-scale HR datasets along with real-time chatbot-generated appraisal data. Experimental results demonstrate superior performance with 92.1%+ accuracy, high cluster separability (Silhouette Score = 0.95), and significant improvements in HR efficiency, participation, and fairness. The framework supports inclusive, data-driven talent management in industrial environments.
Keywords: Mixture of Experts, Workforce Segmentation
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 13, Issue 4 |
| Pages | 1713–1724 |
| Publication date | May 8, 2026 |
| DOI | 10.51244/IJRSI.2026.1304000149 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Dr. Leena S More (Deshmukh), & Dr. Binod Kumar (2026). Mixture of Experts (MoE) Based Top Performer Segmentation with Multilingual Chatbot Integration. International Journal of Research and Scientific Innovation (IJRSI), 13(4), 1713-1724. https://doi.org/10.51244/IJRSI.2026.1304000149
BibTeX
@article{Dr2026,
title = {Mixture of Experts (MoE) Based Top Performer Segmentation with Multilingual Chatbot Integration},
author = {Dr. Leena S More (Deshmukh) and Dr. Binod Kumar},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {13},
number = {4},
pages = {1713--1724},
year = {2026},
doi = {10.51244/IJRSI.2026.1304000149},
publisher = {RSIS International}
}